@inproceedings{schluter-2015-effects,
title = "Effects of Graph Generation for Unsupervised Non-Contextual Single Document Keyword Extraction",
author = "Schluter, Natalie",
editor = "Lecarpentier, Jean-Marc and
Lucas, Nadine",
booktitle = "Actes de la 22e conf{\'e}rence sur le Traitement Automatique des Langues Naturelles. Articles courts",
month = jun,
year = "2015",
address = "Caen, France",
publisher = "ATALA",
url = "https://aclanthology.org/2015.jeptalnrecital-court.10/",
pages = "61--67",
abstract = "This paper presents an exhaustive study on the generation of graph input to unsupervised graph-based non-contextual single document keyword extraction systems. A concrete hypothesis on concept coordination for documents that are scientific articles is put forward, consistent with two separate graph models : one which is based on word adjacency in the linear text{--}an approach forming the foundation of all previous graph-based keyword extraction methods, and a novel one that is based on word adjacency modulo their modifiers. In doing so, we achieve a best reported NDCG score to date of 0.431 for any system on the same data. In terms of a best parameter f-score, we achieve the highest reported to date (0.714) at a reasonable ranked list cut-off of n = 6, which is also the best reported f-score for any keyword extraction or generation system in the literature on the same data. The best-parameter f-score corresponds to a reduction in error of 12.6{\%} conservatively."
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<abstract>This paper presents an exhaustive study on the generation of graph input to unsupervised graph-based non-contextual single document keyword extraction systems. A concrete hypothesis on concept coordination for documents that are scientific articles is put forward, consistent with two separate graph models : one which is based on word adjacency in the linear text–an approach forming the foundation of all previous graph-based keyword extraction methods, and a novel one that is based on word adjacency modulo their modifiers. In doing so, we achieve a best reported NDCG score to date of 0.431 for any system on the same data. In terms of a best parameter f-score, we achieve the highest reported to date (0.714) at a reasonable ranked list cut-off of n = 6, which is also the best reported f-score for any keyword extraction or generation system in the literature on the same data. The best-parameter f-score corresponds to a reduction in error of 12.6% conservatively.</abstract>
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%0 Conference Proceedings
%T Effects of Graph Generation for Unsupervised Non-Contextual Single Document Keyword Extraction
%A Schluter, Natalie
%Y Lecarpentier, Jean-Marc
%Y Lucas, Nadine
%S Actes de la 22e conférence sur le Traitement Automatique des Langues Naturelles. Articles courts
%D 2015
%8 June
%I ATALA
%C Caen, France
%F schluter-2015-effects
%X This paper presents an exhaustive study on the generation of graph input to unsupervised graph-based non-contextual single document keyword extraction systems. A concrete hypothesis on concept coordination for documents that are scientific articles is put forward, consistent with two separate graph models : one which is based on word adjacency in the linear text–an approach forming the foundation of all previous graph-based keyword extraction methods, and a novel one that is based on word adjacency modulo their modifiers. In doing so, we achieve a best reported NDCG score to date of 0.431 for any system on the same data. In terms of a best parameter f-score, we achieve the highest reported to date (0.714) at a reasonable ranked list cut-off of n = 6, which is also the best reported f-score for any keyword extraction or generation system in the literature on the same data. The best-parameter f-score corresponds to a reduction in error of 12.6% conservatively.
%U https://aclanthology.org/2015.jeptalnrecital-court.10/
%P 61-67
Markdown (Informal)
[Effects of Graph Generation for Unsupervised Non-Contextual Single Document Keyword Extraction](https://aclanthology.org/2015.jeptalnrecital-court.10/) (Schluter, JEP/TALN/RECITAL 2015)
ACL